In order to achieve three-dimensional co-registration of images acquired with different technologies, corresponding landmarks must be identified in the respective images. The scale and orientation differences associated with image representations from different scanners present a major obstacle in this task. Visual identification of corresponding regions introduces subjectivity and inconsistency, and may become too laborious for large study series. Therefore, the aim of this research is the development of semi- or fully-automated computer methods for the identification and delineation ("segmentation") of important areas and landmarks (e.g. skull, gray and white brain matter,CSF, pathologic tissues) in brain images derived from different modalities. A fully-automated ("unsupervised") procedure to segment CT images into regions of bone, brain parenchyma, and CSF has been implemented. Calibration of this procedure with a CT image of an anthropomorphic phantom demonstrated unbiased segmentation. Its application for the segmentation of CSF spaces in alcoholic and normal subjects yielded results consistent with a subjective segmentation. For the purpose of enabling the registration of images that represent local glucose utilization (PET) with structural images (MRI or CT), algorithms for the detection of anatomic landmarks in PET slices were developed. Reliable automated detection of the outlines of the skull and the midlines in each slice was achieved, demonstrating suitability of these landmarks for registration.